{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictive Maintenance - Predicting when a machine will break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 - Introduction\n",
    "\n",
    "We will consider that a manufacturing company uses many machines to build their final products. The factory is using IoT technologies via smart sensors to measure and save various kind of inputs from the physical environment and the state of their equipment.\n",
    "\n",
    "Unfortunately, every time a machine breaks the production is stopped, which costs the company thousands of dollars in repair and late delivery fees. The factory manager asks the company Data Science team if it is possible to find a way to be more pro-active so as to optimize spending."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 - Exploratory Data Analysis\n",
    "\n",
    "### 2.1 - Dataset overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n",
      "The raw_dataset has the following shape: (1000, 7).\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lifetime</th>\n",
       "      <th>broken</th>\n",
       "      <th>pressureInd</th>\n",
       "      <th>moistureInd</th>\n",
       "      <th>temperatureInd</th>\n",
       "      <th>team</th>\n",
       "      <th>provider</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>0</td>\n",
       "      <td>92.178854</td>\n",
       "      <td>104.230204</td>\n",
       "      <td>96.517159</td>\n",
       "      <td>TeamA</td>\n",
       "      <td>Provider4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>81</td>\n",
       "      <td>1</td>\n",
       "      <td>72.075938</td>\n",
       "      <td>103.065701</td>\n",
       "      <td>87.271062</td>\n",
       "      <td>TeamC</td>\n",
       "      <td>Provider4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>96.272254</td>\n",
       "      <td>77.801376</td>\n",
       "      <td>112.196170</td>\n",
       "      <td>TeamA</td>\n",
       "      <td>Provider1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   lifetime  broken  pressureInd  moistureInd  temperatureInd   team  \\\n",
       "0        56       0    92.178854   104.230204       96.517159  TeamA   \n",
       "1        81       1    72.075938   103.065701       87.271062  TeamC   \n",
       "2        60       0    96.272254    77.801376      112.196170  TeamA   \n",
       "\n",
       "    provider  \n",
       "0  Provider4  \n",
       "1  Provider4  \n",
       "2  Provider1  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing modules\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from pysurvival.datasets import Dataset\n",
    "%pylab inline\n",
    "\n",
    "# Reading the dataset\n",
    "raw_dataset = Dataset('maintenance').load()\n",
    "print(\"The raw_dataset has the following shape: {}.\".format(raw_dataset.shape))\n",
    "raw_dataset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 7 columns):\n",
      "lifetime          1000 non-null int64\n",
      "broken            1000 non-null int64\n",
      "pressureInd       1000 non-null float64\n",
      "moistureInd       1000 non-null float64\n",
      "temperatureInd    1000 non-null float64\n",
      "team              1000 non-null object\n",
      "provider          1000 non-null object\n",
      "dtypes: float64(3), int64(2), object(2)\n",
      "memory usage: 54.8+ KB\n"
     ]
    }
   ],
   "source": [
    "raw_dataset.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 - From Categorical to numerical\n",
    "There are 3 numerical features (`pressureInd`, `moistureInd`, `temperatureInd`) and 2 categorical features (`team`, `provider`). Let's encode the categorical variables as one-hot vectors and define the modeling features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the time and event column\n",
    "time_column = 'lifetime'\n",
    "event_column = 'broken'\n",
    "\n",
    "# Encoding the categorical variables as one-hot vectors\n",
    "categories = ['provider', 'team']\n",
    "dataset = pd.get_dummies(raw_dataset, columns = categories, drop_first=True)\n",
    "\n",
    "# Defining the modeling features\n",
    "features = np.setdiff1d(dataset.columns, ['lifetime', 'broken']).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 - Null values and duplicates\n",
    "The first thing to do is checking if the raw_dataset contains Null values and has duplicated rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The raw_dataset contains 0 null values\n",
      "The raw_dataset contains 0 duplicates\n"
     ]
    }
   ],
   "source": [
    "# Checking for null values\n",
    "N_null = sum(dataset[features].isnull().sum())\n",
    "print(\"The raw_dataset contains {} null values\".format(N_null)) #0 null values\n",
    "\n",
    "# Removing duplicates if there exist\n",
    "N_dupli = sum(dataset.duplicated(keep='first'))\n",
    "dataset = dataset.drop_duplicates(keep='first').reset_index(drop=True)\n",
    "print(\"The raw_dataset contains {} duplicates\".format(N_dupli))\n",
    "\n",
    "# Number of samples in the dataset\n",
    "N = dataset.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 - Visual exploration and statistics\n",
    "\n",
    "Let's check out/visualize the feature statistics:\n",
    "\n",
    "#### 2.4.1 - Numerical features\n",
    "We will display the boxplot and histogram of each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VPZLkhW35y4FTgVcALwL+PslLq+pH3Q5ckiRJktTY5R27qroWuG9S8W8AZ1fVI22dbW35MuDiqnqkqm6lGfnr+C7GK0mSJEmaZLqjYr4U+Pkka4AfAr9XVf8CzAOu66g33pY9Q5IVwAqAo48+epphSJIkaW+y4MwrZ3yM284+pQuRSPuW6Q6esj9wGHAC8PvAJUmyJweoqrVVtbiqFo+MjEwzDEmSJEnSdBO7ceBz1fgy8ARwOLAVOKqj3vy2TJIkSZI0S6ab2F0GLAVI8lJgDnAvcAVwapLnJDkGOBb4cjcClSRJkiRNbXemOxgD/hl4WZLxJKPAeuAn2ikQLgZOa+/e3QRcAnwT+CJwhiNiSk83NjbGokWL2G+//Vi0aBFjY2P9DkmSJEkDbpeDp1TV8h1s+tUd1F8DrJlJUNKwGhsbY/Xq1axbt44lS5awceNGRkdHAVi+fEd/apIkSdLOTbcrpqRpWLNmDevWrWPp0qU8+9nPZunSpaxbt441a/wuRJIkSdNnYif10ObNm1myZMnTypYsWcLmzZv7FJEkSZKGgYmd1EMLFy5k48aNTyvbuHEjCxcu7FNEkiRJGgYmdlIPrV69mtHRUTZs2MBjjz3Ghg0bGB0dZfXq1f0OTZIkSQNsl4OnSOqe7QOkrFy5ks2bN7Nw4ULWrFnjwCmSJEmaERM7aYaSTHvfm266ibe//e28/e1v3+N9q2rarytJkqThYmInzdB0E6wkJmeSJEnqCp+xkyRJkqQBZ2InSdI0JVmfZFuSGzvKzk3yrSRfT/JXSeZ2bFuVZEuSbyd5fX+iliQNIxM7SZKm70Lg5EllVwGLquqngH8DVgEkeTlwKvCKdp8/S7Jf70KVJA0zEztJkqapqq4F7ptU9ndV9Xi7eh0wv11eBlxcVY9U1a3AFuD4ngUrSRpqJnaSJM2e04G/aZfnAXd2bBtvy54hyYokm5JsmpiYmOUQJUnDwMROkqRZkGQ18Djw6T3dt6rWVtXiqlo8MjLS/eAkSUPH6Q4kSeqyJO8C3gScVE/Na7IVOKqj2vy2TJKkGfOOnSRJXZTkZOD9wJur6uGOTVcApyZ5TpJjgGOBL/cjRknS8PGOnSRJ05RkDDgRODzJOHAWzSiYzwGuSgJwXVW9p6puSnIJ8E2aLppnVNWP+hO5JGnYmNhJkjRNVbV8iuJ1O6m/BlgzexFJkvZVdsWUJEmSpAFnYidJkiRJA26XiV2S9Um2Jblxim3vS1JJDm/Xk+QTSbYk+XqS42YjaEmSJEnSU3bnjt2FwMmTC5McBfwScEdH8RtoRvk6FlgBXDDzECVJkiRJO7PLxK6qrgXum2LTx2mGc66OsmXAp6pxHTA3yZFdiVSSJEmSNKVpPWOXZBmwtaq+NmnTPODOjvXxtmyqY6xIsinJpomJiemEIUmSJEliGoldkoOADwJ/OJMXrqq1VbW4qhaPjIzM5FCSJEmStE+bzjx2LwaOAb7WTrw6H/hKkuOBrcBRHXXnt2WSJElSzyw488quHOe2s0/pynGk2bbHd+yq6htV9cKqWlBVC2i6Wx5XVfcAVwDvbEfHPAF4sKru7m7IkiRJkqROuzPdwRjwz8DLkownGd1J9S8AtwBbgP8B/GZXopQkSZIk7dAuu2JW1fJdbF/QsVzAGTMPS5IkSfuqbnWjlPYl0xoVU5IkSZK09zCxkyRJkqQBZ2InSZIkSQPOxE6SJEmSBpyJnSRJkiQNOBM7SZIkSRpwJnaSJEmSNOBM7CRJkiRpwJnYSZIkSdKAM7GTJGmakqxPsi3JjR1lhyW5KsnN7e9D2/Ik+USSLUm+nuS4/kUuSRo2JnaSJE3fhcDJk8rOBK6uqmOBq9t1gDcAx7Y/K4ALehSjJGkfYGInSdI0VdW1wH2TipcBF7XLFwFv6Sj/VDWuA+YmObI3kUqShp2JnSRJ3XVEVd3dLt8DHNEuzwPu7Kg33pY9Q5IVSTYl2TQxMTF7kUqShoaJnSRJs6SqCqhp7Le2qhZX1eKRkZFZiEySNGxM7CRJ6q7vbO9i2f7e1pZvBY7qqDe/LZMkacZM7CRJ6q4rgNPa5dOAyzvK39mOjnkC8GBHl01JkmZk/34HIEnSoEoyBpwIHJ5kHDgLOBu4JMkocDvwtrb6F4A3AluAh4F39zxgSdLQMrGTJGmaqmr5DjadNEXdAs6Y3YgkSfsqu2JKkiRJ0oDbZWKXZH2SbUlu7Cg7N8m3knw9yV8lmduxbVWSLUm+neT1sxW4JEmSJKmxO3fsLgROnlR2FbCoqn4K+DdgFUCSlwOnAq9o9/mzJPt1LVpJkiRJ0jPsMrGrqmuB+yaV/V1VPd6uXkczZDPAMuDiqnqkqm6leUD8+C7GK0mSJEmapBvP2J0O/E27PA+4s2PbeFsmSZIkSZolM0rskqwGHgc+PY19VyTZlGTTxMTETMKQJEmSpH3atBO7JO8C3gS8ox3CGWArcFRHtflt2TNU1dqqWlxVi0dGRqYbhiRJkiTt86aV2CU5GXg/8Oaqerhj0xXAqUmek+QY4FjgyzMPU5IkSZK0I7ucoDzJGHAicHiSceAsmlEwnwNclQTguqp6T1XdlOQS4Js0XTTPqKofzVbwkiRJkqTdSOyqavkUxet2Un8NsGYmQUmSJEmSdl83RsWUJEmSJPWRiZ0kSZIkDTgTO0mSJEkacCZ2kiRJkjTgTOwkSZIkacDtclRMSZIkzZ4FZ17Z7xAkDQETO6l12GGHcf/99/f0Ndt5IHvm0EMP5b777uvpa0qSJGn2mdhJrfvvv5+q6ncYs6rXiaQkSZJ6w2fsJEmSJGnAmdhJkjQLkrw3yU1JbkwyluSAJMckuT7JliSfSTKn33FKkoaDiZ0kSV2WZB7wW8DiqloE7AecCnwU+HhVvQS4HxjtX5SSpGFiYidJ0uzYHzgwyf7AQcDdwGuBS9vtFwFv6VNskqQhY2InSVKXVdVW4GPAHTQJ3YPADcADVfV4W20cmNefCCVJw8bETpKkLktyKLAMOAZ4EXAwcPIe7L8iyaYkmyYmJmYpSknSMDGxkySp+14H3FpVE1X1GPA54DXA3LZrJsB8YOtUO1fV2qpaXFWLR0ZGehOxJGmgmdhJktR9dwAnJDkozQSSJwHfBDYAb23rnAZc3qf4JElDxsROkqQuq6rraQZJ+QrwDZr2di3wAeB3k2wBXgCs61uQkqShsv+uq0iSpD1VVWcBZ00qvgU4vg/hSJKGnHfsJEmSJGnAmdhJkiRJ0oDbZWKXZH2SbUlu7Cg7LMlVSW5ufx/alifJJ5JsSfL1JMfNZvCSJEmSpN27Y3chz5x750zg6qo6Fri6XQd4A3Bs+7MCuKA7YUqSJEmSdmSXiV1VXQvcN6l4GXBRu3wR8JaO8k9V4zqa+XqO7FawkiRJkqRnmu4zdkdU1d3t8j3AEe3yPODOjnrjbdkzJFmRZFOSTRMTE9MMQ5IkSZI048FTqqqAmsZ+a6tqcVUtHhkZmWkYkiRJkrTPmm5i953tXSzb39va8q3AUR315rdlkiRJkqRZMt3E7grgtHb5NODyjvJ3tqNjngA82NFlU5IkSZI0C/bfVYUkY8CJwOFJxoGzgLOBS5KMArcDb2urfwF4I7AFeBh49yzELM2KOuv58KFD+h3GrKqznt/vECRJkjQLdpnYVdXyHWw6aYq6BZwx06CkfsiHv0fzT3h4JaE+1O8oJEmS1G0zHjxFkiRJktRfJnaSJEmSNOBM7CRJkiRpwJnYSZIkSdKAM7GTJEmSpAG3y1ExJUmSpH3VgjOvnPExbjv7lC5EIu2cd+wkSZIkacCZ2EmSJEnSgDOxkyRJkqQBZ2InSdIsSDI3yaVJvpVkc5JXJzksyVVJbm5/H9rvOCVJw8HETpKk2XEe8MWq+knglcBm4Ezg6qo6Fri6XZckacZM7CRJ6rIkhwC/AKwDqKpHq+oBYBlwUVvtIuAt/YlQkjRsTOwkSeq+Y4AJ4M+TfDXJJ5McDBxRVXe3de4Bjphq5yQrkmxKsmliYqJHIUuSBpmJnSRJ3bc/cBxwQVX9NPAQk7pdVlUBNdXOVbW2qhZX1eKRkZFZD1aSNPicoFzqkKTfIcyqQw91nAapR8aB8aq6vl2/lCax+06SI6vq7iRHAtv6FqEkaaiY2Emt5svz3knS89eU1BtVdU+SO5O8rKq+DZwEfLP9OQ04u/19eR/DlCQNERM7SZJmx0rg00nmALcA76Z5BOKSJKPA7cDb+hifJGmImNhJkjQLqupfgcVTbDqp17FIkoafg6dIkiRJ0oCbUWKX5L1JbkpyY5KxJAckOSbJ9Um2JPlM2wVFkiRJkjRLpp3YJZkH/BawuKoWAfsBpwIfBT5eVS8B7gdGuxGoJEmSJGlqM33Gbn/gwCSPAQcBdwOvBd7ebr8I+BBwwQxfR5Ikaa+z4Mwr+x2CJAEzuGNXVVuBjwF30CR0DwI3AA9U1eNttXFg3lT7J1mRZFOSTRMTE9MNQ5IkSZL2eTPpinkosAw4BngRcDBw8u7uX1Vrq2pxVS0eGRmZbhiSJEmStM+byeAprwNuraqJqnoM+BzwGmBuku1dPOcDW2cYoyRJkiRpJ2byjN0dwAlJDgL+nWZenk3ABuCtwMXAacDlMw1SkiRJGlTdeBbztrNP6UIkGmYzecbueuBS4CvAN9pjrQU+APxuki3AC4B1XYhTkiRJkrQDMxoVs6rOAs6aVHwLcPxMjitJkiRJ2n0zmqBckiRJktR/JnaSJEmSNOBM7CRJkiRpwJnYSZIkSdKAM7GTJEmSpAFnYidJkiRJA87ETpIkSZIGnImdJEmSJA04EztJkiRJGnAmdpIkSZI04EzsJEmaJUn2S/LVJJ9v149Jcn2SLUk+k2ROv2OUJA0HEztJkmbPbwObO9Y/Cny8ql4C3A+M9iUqSdLQMbGTJGkWJJkPnAJ8sl0P8Frg0rbKRcBb+hOdJGnYmNhJkjQ7/gR4P/BEu/4C4IGqerxdHwfmTbVjkhVJNiXZNDExMfuRSpIGnomdJEldluRNwLaqumE6+1fV2qpaXFWLR0ZGuhydJGkY7d/vACRJGkKvAd6c5I3AAcDzgfOAuUn2b+/azQe29jFGSdIQ8Y6dJEldVlWrqmp+VS0ATgWuqap3ABuAt7bVTgMu71OIkqQhY2InSVLvfAD43SRbaJ65W9fneCRJQ8KumJIkzaKq+hLwpXb5FuD4fsYjSRpOM7pjl2RukkuTfCvJ5iSvTnJYkquS3Nz+PrRbwUqSJEmSnmmmXTHPA75YVT8JvJJmEtYzgaur6ljg6nZdkiRJkjRLpp3YJTkE+AXa5wOq6tGqegBYRjPpKjj5qiRJkiTNupncsTsGmAD+PMlXk3wyycHAEVV1d1vnHuCIqXZ28lVJkiRJ6o6ZJHb7A8cBF1TVTwMPManbZVUVUFPt7OSrkiRJktQdM0nsxoHxqrq+Xb+UJtH7TpIjAdrf22YWoiRJkiRpZ6ad2FXVPcCdSV7WFp0EfBO4gmbSVXDyVUmSJEmadTOdx24l8Okkc4BbgHfTJIuXJBkFbgfeNsPXkCRJkiTtxIwSu6r6V2DxFJtOmslxJUmSJEm7b6bz2EmSJEmS+szETpIkSZIGnImdJEmSJA24mQ6eIkmSNHAWnHllv0OQpK7yjp0kSZIkDTgTO0mSJEkacHbFlCRJkvZy3eg+fNvZp3QhEu2tvGMnSZIkSQPOxE6SJEmSBpyJnSRJkiQNOBM7SZK6LMlRSTYk+WaSm5L8dlt+WJKrktzc/j6037FKkoaDiZ0kSd33OPC+qno5cAJwRpKXA2cCV1fVscDV7bokSTNmYidJUpdV1d1V9ZV2+fvAZmAesAy4qK12EfCW/kQoSRo2TncgzVCSvuxbVdPeV1LvJFkA/DRwPXBEVd3dbroHOGIH+6wAVgAcffTRsx+kJGngecdOmqGq6suPpL1fkucCfwn8TlV9r3NbNX/IU/4xV9XaqlpcVYtHRkZ6EKkkadCZ2EmSNAuSPJsmqft0VX2uLf5OkiPb7UcC2/oVnyRpuJjYST02NjbGokWL2G+//Vi0aBFjY2P9DklSl6XpZ70O2FxVf9yx6QrgtHb5NODyXscmSRpOPmMn9dDY2BirV69m3bp1LFkbxqNpAAAKV0lEQVSyhI0bNzI6OgrA8uXL+xydpC56DfBrwDeS/Gtb9kHgbOCSJKPA7cDb+hSfJGnImNhJPbRmzRrWrVvH0qVLAVi6dCnr1q1j5cqVJnbSEKmqjcCORkc6qZexSNJ2C868sivHue3sU7pyHHWXXTGlHtq8eTNLlix5WtmSJUvYvHlznyKSJEnSMJhxYpdkvyRfTfL5dv2YJNcn2ZLkM0nmzDxMaTgsXLiQD3/4w097xu7DH/4wCxcu7HdokiRJGmDduGP32zQTr273UeDjVfUS4H5gtAuvIQ2FpUuX8pGPfIR7772XJ554gnvvvZePfOQjT3bNlCRJkqZjRs/YJZkPnAKsAX63HQXstcDb2yoXAR8CLpjJ60jD4rLLLuN5z3seBx54IM961rM48MADed7znsdll13G+eef3+/wJGkgdOs5IUkaJjO9Y/cnwPuBJ9r1FwAPVNXj7fo4MG+qHZOsSLIpyaaJiYkZhiENhvHxcT772c9y66238qMf/Yhbb72Vz372s4yPj/c7NEmSJA2waSd2Sd4EbKuqG6azf1WtrarFVbV4ZGRkumFIA+eaa6552jN211xzTb9DkiRJ0oCbyR271wBvTnIbcDFNF8zzgLlJtnfxnA9snVGE0hA57LDDOPfcczn99NP5/ve/z+mnn865557LYYcd1u/QJEmSNMCmndhV1aqqml9VC4BTgWuq6h3ABuCtbbXTgMtnHKU0JA466CDmzJnDmWeeycEHH8yZZ57JnDlzOOigg/odmiRJkgbYbMxj9wGagVS20Dxzt24WXkMaSFu3buXggw9m3rx5POtZz2LevHkcfPDBbN3qjW1JkiRNX1cSu6r6UlW9qV2+paqOr6qXVNV/qqpHuvEa0jCYM2cOq1atetrgKatWrWLOHKd7lCRJ0vTNaLoDSXvm0Ucf5eyzz+b888/njjvu4Oijj+ahhx7i0Ucf7XdokiRJGmAmdlIPzZs3jx/84AcAVBUAjz32GPPmTTkriCRJ0l6nG3NJ3nb2KV2IRJ1m4xk7STtxwAEHsH79eh555BHWr1/PAQcc0O+QJEmSNOBM7KQeuuuuuzjnnHNYuXIlBxxwACtXruScc87hrrvu6ndokiRJGmB2xZR6aOHChcyfP58bb7zxybINGzawcOHCPkYlSb3TjS5ckqRn8o6d1EOrV69mdHSUDRs28Nhjj7FhwwZGR0dZvXp1v0OTJEnSAPOOndRDy5cvB2DlypVs3ryZhQsXsmbNmifLJWk2ONCBJA0/Ezupx5YvX24iJ0mSpK6yK6YkSZIkDTgTO0mSJEkacHbFlCSph5KcDJwH7Ad8sqrO7nNIu8XRLCUNq2F5DtnETpKkHkmyH/DfgP8IjAP/kuSKqvpmfyOTpN4almRqb2JXTEmSeud4YEtV3VJVjwIXA8v6HJMkaQjsFXfsbrjhhnuT3N7vOKQeOxy4t99BSH3w4/0OoI/mAXd2rI8DPzu5UpIVwIp29QdJvt2D2HZmWD6vhuE8huEcYDjOYxjOAQb4PPLRJxf7fg4dsczEy2ay816R2FXVSL9jkHotyaaqWtzvOCTtfapqLbC233FsNyyfV8NwHsNwDjAc5zEM5wDDcR7DcA7QnMdM9rcrpiRJvbMVOKpjfX5bJknSjJjYSZLUO/8CHJvkmCRzgFOBK/ockyRpCOwVXTGlfdRe081KUm9U1eNJ/jPwtzTTHayvqpv6HNbuGJbPq2E4j2E4BxiO8xiGc4DhOI9hOAeY4XmkqroViCRJkiSpD+yKKUmSJEkDzsROkiRJkgaciZ3UY0nWJ9mW5MZ+xyJJU0myX5KvJvl8u35MkuuTbEnymXbgl71akrlJLk3yrSSbk7w6yWFJrkpyc/v70H7HuTNJ3pvkpiQ3JhlLcsAgXIup2rkdvfdpfKI9n68nOa5/kT/dDs7j3Pbf1NeT/FWSuR3bVrXn8e0kr+9P1E+3s/9zJHlfkkpyeLs+UNeiLV/ZXo+bkpzTUT4Q1yLJq5Jcl+Rfk2xKcnxbPq1rYWIn9d6FwMn9DkKSduK3gc0d6x8FPl5VLwHuB0b7EtWeOQ/4YlX9JPBKmvM5E7i6qo4Frm7X90pJ5gG/BSyuqkU0g+2cymBciwt5Zju3o/f+DcCx7c8K4IIexbg7LuSZ53EVsKiqfgr4N2AVQJKX01yfV7T7/FmS/XoX6g5dyBT/50hyFPBLwB0dxQN1LZIsBZYBr6yqVwAfa8sH6VqcA3y4ql4F/GG7DtO8FiZ2Uo9V1bXAff2OQ5KmkmQ+cArwyXY9wGuBS9sqFwFv6U90uyfJIcAvAOsAqurRqnqA5j+BF7XV9vrzoBm9/MAk+wMHAXczANdiB+3cjt77ZcCnqnEdMDfJkb2JdOemOo+q+ruqerxdvY5mLkpozuPiqnqkqm4FtgDH9yzYHdjJ/zk+Drwf6BxFcaCuBfAbwNlV9UhbZ1tbPkjXooDnt8uHAHe1y9O6FiZ2kiSp05/Q/IfviXb9BcADHf+ZHQfm9SOwPXAMMAH8edul9JNJDgaOqKq72zr3AEf0LcJdqKqtNHcg7qBJ6B4EbmDwrsV2O3rv5wF3dtQbpHM6HfibdnlgziPJMmBrVX1t0qaBOYfWS4Gfb7sm/0OS/9CWD9J5/A5wbpI7af7eV7Xl0zoHEztJkgRAkjcB26rqhn7HMkP7A8cBF1TVTwMPManbZTXzPe21cz61z6Ato0lSXwQczJB049/b3/vdkWQ18Djw6X7HsieSHAR8kKbb36DbHzgMOAH4feCStofBIPkN4L1VdRTwXtpeBtNlYidJkrZ7DfDmJLcBF9N0+zuPphvQ/m2d+cDW/oS328aB8aq6vl2/lCbR+8727kzt72072H9v8Drg1qqaqKrHgM/RXJ9Buxbb7ei93woc1VFvrz+nJO8C3gS8o56aEHpQzuPFNF8WfK39O58PfCXJjzE457DdOPC5trvil2l6GRzOYJ3HaTR/2wCf5akuo9M6BxM7SZIEQFWtqqr5VbWAZvCBa6rqHcAG4K1ttdOAy/sU4m6pqnuAO5O8rC06CfgmcAVN/LD3n8cdwAlJDmrvQmw/h4G6Fh129N5fAbyzHQXwBODBji6be50kJ9N0VX5zVT3csekK4NQkz0lyDM2gF1/uR4w7U1XfqKoXVtWC9u98HDiu/ZsZqGsBXAYsBUjyUmAOcC8Dci1adwG/2C6/Fri5XZ7Wtdh/VxUkdVeSMeBE4PAk48BZVTWjW++SNMs+AFyc5I+ArzLD7kI9shL4dJrpAG4B3k3zhfYlSUaB24G39TG+naqq65NcCnyFpsvfV4G1wJXs5ddiqnYOOJup3/svAG+kGeDiYZrrtFfYwXmsAp4DXNX2+ruuqt5TVTcluYQm+X4cOKOqftSfyJ+yh//nGLRrsR5Y304f8ChwWnsHdWCuBfDrwHntXfgf0oyACdO8FnnqDrIkSZIkaRDZFVOSJEmSBpyJnSRJkiQNOBM7SZIkSRpwJnaSJEmSNOBM7CRJkiRpwJnYSZIkSdKAM7GTJEmSpAH3/wNa1qV0Tf/U5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "for feature in ['pressureInd','moistureInd','temperatureInd']:\n",
    "\n",
    "    # Creating an empty chart\n",
    "    fig, ((ax1, ax2)) = plt.subplots(1, 2,  figsize=(15, 4))\n",
    "\n",
    "    # Extracting the feature values\n",
    "    x = raw_dataset[feature].values\n",
    "\n",
    "    # Boxplot\n",
    "    ax1.boxplot(x)\n",
    "    ax1.set_title( 'Boxplot for {}'.format(feature) )\n",
    "\n",
    "    # Histogram\n",
    "    ax2.hist(x, bins=20)\n",
    "    ax2.set_title( 'Histogram for {}'.format(feature) )\n",
    "\n",
    "    # Display\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These features have very few outliers, so there's no real need to remove them, and they seem to follow normal distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4.2 - Categorical features\n",
    "We will display the occurrences of the categories in a barchart for each feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "for feature in ['team', 'provider']:\n",
    "\n",
    "    # Creating an empty chart\n",
    "    fig, ax = plt.subplots(figsize=(15, 4))\n",
    "\n",
    "    # Extracting the feature values\n",
    "    x = raw_dataset[feature].values\n",
    "\n",
    "    # Counting the number of occurrences for each category\n",
    "    data = Counter(x)\n",
    "    category = list(data.keys())\n",
    "    counts = list(data.values())\n",
    "\n",
    "    # Boxplot\n",
    "    ax.bar(category, counts)\n",
    "\n",
    "    # Display\n",
    "    plt.title( 'Barchart for {}'.format(feature) )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These features seem to be uniformly distributed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4.3 - Time & Event\n",
    "We will display the occurrences of event and censorship, as well as the distribution of the time output variable for both situations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating an empty chart\n",
    "fig, ((ax1, ax2)) = plt.subplots(1, 2,  figsize=(15, 4))\n",
    "\n",
    "# Counting the number of occurrences for each category \n",
    "data = Counter(raw_dataset['broken'].replace({0:'not broken yet', 1:'broken'}))\n",
    "category = list(data.keys())\n",
    "counts = list(data.values())\n",
    "idx = range(len(counts))\n",
    "\n",
    "# Displaying the occurrences of the event/censoring\n",
    "ax1.bar(idx, counts)\n",
    "ax1.set_xticks(idx)\n",
    "ax1.set_xticklabels(category)\n",
    "ax1.set_title( 'occurrences of the event/censoring', fontsize=15)\n",
    "\n",
    "# Showing the histogram of the survival times for the censoring\n",
    "time_0 = raw_dataset.loc[ raw_dataset['broken'] == 0, 'lifetime']\n",
    "ax2.hist(time_0, bins=30, alpha=0.3, color='blue', label = 'not broken yet')\n",
    "\n",
    "# Showing the histogram of the survival times for the events\n",
    "time_1 = raw_dataset.loc[ raw_dataset['broken'] == 1, 'lifetime']\n",
    "ax2.hist(time_1, bins=20, alpha=0.7, color='black', label = 'broken')\n",
    "ax2.set_title( 'Histogram - survival time', fontsize=15)\n",
    "\n",
    "# Displaying everything side-by-side\n",
    "plt.legend(fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that 2/3 of the data is censored and that the failures start happening when the machine has been active for at least 60 weeks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4.4 - Correlations\n",
    "Let's compute and visualize the correlation between the features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysurvival.utils.display import correlation_matrix\n",
    "correlation_matrix(dataset[features], figure_size=(15, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, there aren't any alarming correlations.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 - Modeling\n",
    "So as to perform cross-validation later on and assess the performances of the model, let's split the dataset into training and testing sets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 - Splitting between training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building training and testing sets\n",
    "from sklearn.model_selection import train_test_split\n",
    "index_train, index_test = train_test_split( range(N), test_size = 0.3)\n",
    "data_train = dataset.loc[index_train].reset_index( drop = True )\n",
    "data_test  = dataset.loc[index_test].reset_index( drop = True )\n",
    "\n",
    "# Creating the X, T and E inputs\n",
    "X_train, X_test = data_train[features], data_test[features]\n",
    "T_train, T_test = data_train[time_column], data_test[time_column]\n",
    "E_train, E_test = data_train[event_column], data_test[event_column]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 - Building a Linear MTLR model\n",
    "Let's now fit a Linear MTLR model to the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "% Completion: 100%|***********************************************|Loss: 334.54\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LinearMultiTaskModel"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pysurvival.models.multi_task import LinearMultiTaskModel\n",
    "\n",
    "# Initializing the MTLR with a time axis split into 300 intervals\n",
    "linear_mtlr = LinearMultiTaskModel(bins=300)\n",
    "\n",
    "# Fitting the model\n",
    "linear_mtlr.fit(X_train, T_train, E_train, num_epochs = 1000,\n",
    "                init_method = 'orthogonal', optimizer ='rmsprop', \n",
    "                lr = 1e-3, l2_reg = 4,  l2_smooth = 4, )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take a look at the loss function values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysurvival.utils.display import display_loss_values\n",
    "display_loss_values(linear_mtlr, figure_size=(7, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 - Cross Validation\n",
    "In order to assess the model performance, we previously split the original dataset into training and testing sets, so that we can now compute its performance metrics on the testing set:\n",
    "\n",
    "## 4.1 - C-index\n",
    "The C-index represents the global assessment of the model discrimination power: this is the model’s ability to correctly provide a reliable ranking of the survival times based on the individual risk scores. In general, when the C-index is close to 1, the model has an almost perfect discriminatory power; but it is close to 0.5, it has no ability to discriminate between low and high risk subjects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C-index: 0.91\n"
     ]
    }
   ],
   "source": [
    "from pysurvival.utils.metrics import concordance_index\n",
    "\n",
    "c_index = concordance_index(linear_mtlr, X_test, T_test, E_test)\n",
    "print('C-index: {:.2f}'.format(c_index)) #0.91"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the c-index of the model (0.92 here) is close to 1, it seems that the model will yield satisfactory results in terms of survival times predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 - Brier Score\n",
    "The Brier score measures the average discrepancies between the status and the estimated probabilities at a given time. Thus, the lower the score (usually below 0.25), the better the predictive performance. To assess the overall error measure across multiple time points, the Integrated Brier Score (IBS) is usually computed as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IBS: 0.00\n"
     ]
    }
   ],
   "source": [
    "from pysurvival.utils.display import integrated_brier_score\n",
    "ibs = integrated_brier_score(linear_mtlr, X_test, T_test, E_test, t_max=100, \n",
    "                       figure_size=(20, 6.5) )\n",
    "print('IBS: {:.2f}'.format(ibs)) #0.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IBS is very close to 0.0 on the entire model time axis. This indicates that the model will have very good predictive abilities."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 5 - Predictions\n",
    "\n",
    "## 5.1 - Overall predictions\n",
    "Now that we have built a model that seems to provide great performances, let's compare the time series of the actual and predicted number of machines experiencing a failure, for each time t."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysurvival.utils.display import compare_to_actual\n",
    "results = compare_to_actual(linear_mtlr, X_test, T_test, E_test,\n",
    "                            is_at_risk = False,  figure_size=(16, 6), \n",
    "                            metrics = ['rmse', 'mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* the time series of the actual and predicted number of machines that are still working, for each time t."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysurvival.utils.display import compare_to_actual\n",
    "results = compare_to_actual(linear_mtlr, X_test, T_test, E_test,\n",
    "                            is_at_risk = True,  figure_size=(16, 6), \n",
    "                            metrics = ['rmse', 'mean', 'median'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Both comparisons show that the model do an excellent job predicting the number of machines that are still working or that failed for all times t.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "## 5.2 - Individual predictions\n",
    "Now that we know that we can provide reliable predictions for an entire cohort. Let's Compute the survival predictions at the individual level.\n",
    "\n",
    "First, we can construct the risk groups based on risk scores distribution. The helper function create_risk_groups, which can be found in pysurvival.utils, will help us do that:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pysurvival.utils.display import create_risk_groups\n",
    "risk_groups = create_risk_groups(model=linear_mtlr, X=X_test,\n",
    "    use_log = True, num_bins=25, figure_size=(20, 4),\n",
    "    low={'lower_bound':0, 'upper_bound':5.7, 'color':'red'},\n",
    "    medium={'lower_bound': 5.7, 'upper_bound': 6.7, 'color':'green'},\n",
    "    high={'lower_bound':6.7, 'upper_bound':10, 'color':'blue'}\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we can see that 3 main groups, low, medium and high risk groups, can be created. Because the C-index is high, the model will be able to perfectly rank the survival times of a random unit of each group, such that $t_{high} \\leq t_{medium} \\leq t_{low}$.\n",
    "\n",
    "Let's randomly select individual unit in each group and compare the survival functions. To demonstrate our point, we will purposely select units which experienced an event to visualize the actual time of event."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initializing the figure\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "# Selecting a random individual that experienced an event from each group\n",
    "groups = []\n",
    "for i, (label, (color, indexes)) in enumerate(risk_groups.items()) :\n",
    "\n",
    "    # Selecting the individuals that belong to this group\n",
    "    if len(indexes) == 0 :\n",
    "        continue\n",
    "    X = X_test.values[indexes, :]\n",
    "    T = T_test.values[indexes]\n",
    "    E = E_test.values[indexes]\n",
    "\n",
    "    # Randomly extracting an individual that experienced an event\n",
    "    choices = np.argwhere((E==1.)).flatten()\n",
    "    if len(choices) == 0 :\n",
    "        continue\n",
    "    k = np.random.choice( choices, 1)[0]\n",
    "\n",
    "    # Saving the time of event\n",
    "    t = T[k]\n",
    "\n",
    "    # Computing the Survival function for all times t\n",
    "    survival = linear_mtlr.predict_survival(X[k, :]).flatten()\n",
    "\n",
    "    # Displaying the functions\n",
    "    label_ = '{} risk'.format(label)\n",
    "    plt.plot(linear_mtlr.times, survival, color = color, label=label_, lw=2)\n",
    "    groups.append(label)\n",
    "\n",
    "    # Actual time\n",
    "    plt.axvline(x=t, color=color, ls ='--')\n",
    "    ax.annotate('T={:.1f}'.format(t), xy=(t, 0.5*(1.+0.2*i)), \n",
    "        xytext=(t, 0.5*(1.+0.2*i)), fontsize=12)\n",
    "\n",
    "# Show everything\n",
    "groups_str = ', '.join(groups)\n",
    "title = \"Comparing Survival functions between {} risk grades\".format(groups_str)\n",
    "plt.legend(fontsize=12)\n",
    "plt.title(title, fontsize=15)\n",
    "plt.ylim(0, 1.05)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the model manages to perfectly predict the event time, here it corresponds to a sudden drop in the individual survival function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "# 6 - Conclusion\n",
    "In this example, we have shown that it is possible to predict with great degree of certainty when a machine will fail. The Data Science team could predict the machines survival function every day, so that 1 or 2 weeks before the machine is supposed to fail, the factory manager is notified so that the necessary actions can be taken.\n",
    "\n",
    "We can now save our model so as to put it in production and score future machines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving the model to disk as /Users/xxx/Desktop/pdm_linear_mtlr.zip\n"
     ]
    }
   ],
   "source": [
    "# Let's now save our model\n",
    "from pysurvival.utils import save_model\n",
    "save_model(linear_mtlr, '/Users/xxx/Desktop/pdm_linear_mtlr.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
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